TEACh: Task-driven Embodied Agents that Chat

• URL: http://arxiv.org/abs/2110.00534v1
• Date: Fri, 1 Oct 2021 17:00:14 GMT
• Title: TEACh: Task-driven Embodied Agents that Chat
• Authors: Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramithu, Gokhan Tur, Dilek Hakkani-Tur
• Abstract summary: We introduce TEACh, a dataset of over 3,000 human--human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. We propose three benchmarks using TEACh to study embodied intelligence challenges.
• Score: 14.142543383443032
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human--human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates in natural language with a Follower. The Follower navigates through and interacts with the environment to complete tasks varying in complexity from "Make Coffee" to "Prepare Breakfast", asking questions and getting additional information from the Commander. We propose three benchmarks using TEACh to study embodied intelligence challenges, and we evaluate initial models' abilities in dialogue understanding, language grounding, and task execution.

Related papers

• tagE: Enabling an Embodied Agent to Understand Human Instructions [3.943519623674811]
  We introduce a novel system known as task and argument grounding for Embodied agents (tagE) At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions.
  arXiv  Detail & Related papers  (2023-10-24T08:17:48Z)
• Dobby: A Conversational Service Robot Driven by GPT-4 [22.701223191699412]
  This work introduces a robotics platform which embeds a conversational AI agent in an embodied system for service tasks. The agent is derived from a large language model, which has learned from a vast corpus of general knowledge. In addition to generating dialogue, this agent can interface with the physical world by invoking commands on the robot.
  arXiv  Detail & Related papers  (2023-10-10T04:34:00Z)
• Interactive Conversational Head Generation [68.76774230274076]
  We introduce a new conversation head generation benchmark for synthesizing behaviors of a single interlocutor in a face-to-face conversation. The capability to automatically synthesize interlocutors which can participate in long and multi-turn conversations is vital and offer benefits for various applications.
  arXiv  Detail & Related papers  (2023-07-05T08:06:26Z)
• Collecting Interactive Multi-modal Datasets for Grounded Language Understanding [66.30648042100123]
  We formalized the collaborative embodied agent using natural language task. We developed a tool for extensive and scalable data collection. We collected the first dataset for interactive grounded language understanding.
  arXiv  Detail & Related papers  (2022-11-12T02:36:32Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021 [58.196738777207315]
  We propose emphIGLU: Interactive Grounded Language Understanding in a Collaborative Environment. The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
  arXiv  Detail & Related papers  (2022-05-05T01:20:09Z)
• Training Conversational Agents with Generative Conversational Networks [74.9941330874663]
  We use Generative Conversational Networks to automatically generate data and train social conversational agents. We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
  arXiv  Detail & Related papers  (2021-10-15T21:46:39Z)
• How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus [13.12257465328948]
  We present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment.
  arXiv  Detail & Related papers  (2021-06-11T16:58:22Z)
• Situated Language Learning via Interactive Narratives [16.67845396797253]
  This paper explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language. Two key components in creating such agents are interactivity and environment grounding. We discuss the unique challenges a text games' puzzle-like structure combined with natural language state-and-action spaces provides.
  arXiv  Detail & Related papers  (2021-03-18T01:55:16Z)
• TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue [113.45485470103762]
  In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
  arXiv  Detail & Related papers  (2020-04-15T04:09:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.